#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest

import numpy as np
from op_test import OpTest
from test_fusion_gru_op import fusion_gru
from test_fusion_lstm_op import ACTIVATION


class TestFusionGRUINT8ONEDNNOp(OpTest):
    def set_confs(self):
        pass

    def setUp(self):
        self.op_type = "fusion_gru"
        self.lod = [[2, 4, 3]]
        self.IC = 3
        self.OC = 5
        self.is_reverse = False
        self.with_h0 = False
        self.with_bias = True
        self.act_state = 'tanh'
        self.act_gate = 'sigmoid'
        self.origin_mode = True
        self.use_onednn = True
        self.onednn_data_type = "int8"
        self.force_fp32_output = True
        self.error_margin = 1e-5
        self.set_confs()

        # RNN dimensions
        T = sum(self.lod[0])
        N = len(self.lod[0])

        # Input data
        x_f32 = np.random.rand(T, self.IC).astype('float32') * 2 - 1
        scale_data = 63.0
        shift_data = 64.0
        x_u8 = np.rint(x_f32 * scale_data + shift_data).astype(np.uint8)
        #  x_u8 = (x_f32 * scale_data + shift_data).astype(np.uint8)

        # WeightX/WeightH data
        wx = np.random.rand(self.IC, 3 * self.OC).astype('float32') * 2 - 1
        wh = np.random.rand(self.OC, 3 * self.OC).astype('float32') * 2 - 1

        # Calculating weight scales
        # scales = 63 / max(abs(channel_wise(weightsX + weightsH)))
        # WeightX data shape in PP: [IC, 3 * OC]
        # WeightH data shape in PP: [OC, 2 * OC] + [OC, OC]
        # Scales shape in oneDNN:   [3, OC]
        s8_max = 127.0
        scale_ur = s8_max / np.max(
            np.abs(
                np.concatenate(
                    [
                        wx[:, : 2 * self.OC],
                        wh.flatten()[: 2 * self.OC * self.OC].reshape(
                            self.OC, 2 * self.OC
                        ),
                    ],
                    axis=0,
                )
            ),
            axis=0,
        )
        scale_o = s8_max / np.max(
            np.abs(
                np.concatenate(
                    [
                        wx[:, 2 * self.OC :],
                        wh.flatten()[2 * self.OC * self.OC :].reshape(
                            self.OC, self.OC
                        ),
                    ],
                    axis=0,
                )
            ),
            axis=0,
        )

        scale_weights = np.concatenate([scale_ur, scale_o]).astype('float')

        bias = (
            np.random.rand(1, 3 * self.OC).astype('float32')
            if self.with_bias
            else np.zeros((1, 3 * self.OC), dtype='float32')
        )
        h0 = (
            np.random.rand(N, self.OC).astype('float32')
            if self.with_h0
            else np.zeros((N, self.OC), dtype='float32')
        )

        _, _, _, hidden_f32 = fusion_gru(
            x_f32,
            self.lod,
            h0,
            wx,
            wh,
            bias,
            self.is_reverse,
            self.origin_mode,
            ACTIVATION[self.act_state],
            ACTIVATION[self.act_gate],
        )

        self.inputs = {'X': (x_u8, self.lod), 'WeightX': wx, 'WeightH': wh}

        if self.with_bias:
            self.inputs['Bias'] = bias

        if self.with_h0:
            self.inputs['H0'] = h0

        if self.force_fp32_output:
            self.error_margin = 1e-1
            self.outputs = {'Hidden': (hidden_f32, self.lod)}
        else:
            self.error_margin = 1
            hidden_u8 = np.rint(hidden_f32 * scale_data + shift_data).astype(
                np.uint8
            )
            #  hidden_u8 = (hidden_f32 * scale_data + shift_data).astype(np.uint8)
            self.outputs = {'Hidden': (hidden_u8, self.lod)}

        self.attrs = {
            'activation': self.act_state,
            'gate_activation': self.act_gate,
            'is_reverse': self.is_reverse,
            'origin_mode': self.origin_mode,
            'use_onednn': self.use_onednn,
            'onednn_data_type': self.onednn_data_type,
            'force_fp32_output': self.force_fp32_output,
            'Scale_data': scale_data,
            'Shift_data': shift_data,
            'Scale_weights': scale_weights,
        }

    def test_check_output(self):
        self.check_output(
            check_dygraph=False,
            atol=self.error_margin,
            check_pir_onednn=self.check_pir_onednn,
        )


class TestFusionGRUINT8ONEDNNOp2(TestFusionGRUINT8ONEDNNOp):
    def set_confs(self):
        self.force_fp32_output = False


class TestFusionGRUINT8ONEDNNOp3(TestFusionGRUINT8ONEDNNOp):
    def set_confs(self):
        self.origin_mode = False


class TestFusionGRUINT8ONEDNNOp4(TestFusionGRUINT8ONEDNNOp):
    def set_confs(self):
        self.with_bias = False


class TestFusionGRUINT8ONEDNNOp5(TestFusionGRUINT8ONEDNNOp):
    def set_confs(self):
        self.with_h0 = False


if __name__ == "__main__":
    from paddle import enable_static

    enable_static()
    unittest.main()
